Parsing quality is the biggest lever on retrieval quality.
Cleaner chunks mean better retrieval and fewer hallucinations — without touching your embedding model or prompt.
USE CASE · RAG
If your RAG isn't working, start at the chunks.
Most RAG problems aren't embedding problems — they're parsing problems. Datalab's layout-aware parse produces clean chunks a retriever can actually use, each cited back to its source page.
TRUSTED BY
Get embedding-ready chunks back in one call.
from datalab_sdk import DatalabClient, ConvertOptions
client = DatalabClient()
# "chunks" output returns pre-chunked spans ready for embedding.
result = client.convert(
"document.pdf",
options=ConvertOptions(output_format="chunks", mode="balanced"),
)
# Parse Quality Score (0-5) — skip low-quality parses before indexing.
if result.parse_quality_score < 3.0:
log.warn("Skipping low-quality parse for document.pdf")
else:
# Each span carries block-level provenance back to the source page.
embed_and_index(result.chunks) # your embedding + vector store of choiceWhy teams prefer Datalab for RAG.
Every chunk cites the source block it came from.
Each span carries a block ID back to its page, so citations and debugging happen at the chunk level.
Pin a pipeline version; re-runs produce the same chunks.
Pipeline versions are immutable, so reindexing months later still produces identical chunk boundaries.
Run RAG ingestion wherever your data lives.
- Managed cloud
Start with an API key — nothing to host or operate.
Sign up → - EU data residency
Run in-region, with no egress to US infrastructure.
Sign up → - Your VPC
Runs inside your own AWS, GCP, or Azure account.
Talk to sales → - On-prem & air-gapped
Fully offline, the same model weights, dedicated support.
Talk to sales →
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Better retrieval starts with a cleaner parse.
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